The amount of content on the Internet is growing dramatically. Recommender technology is key to helping people find new, interesting and relevant items in this content. For example, music recommendation systems are increasingly important in the ever-growing world of digital music. A recommender is a tool that recommends one or more items to a user based on one or more provided criteria. Conventional recommenders typically use some form of collaborative filtering that exploits the wisdom of the crowds to make recommendations of the form “people who bought X also bought Y.” A recommender using collaborative filtering generally relies on the names or titles of items to make recommendations. Such a recommender, directed at music, may take the title(s) of music that a user has expressed interest in or purchased, look for other people who have purchased the same title(s), determine one or more other titles that the other people have purchased but the user has not, and recommend one or more of the other titles to the user.
Conventional recommenders that rely on collaborative filtering generally do not provide reasons as to why an item is recommended beyond “Other people who selected item X also selected item Y.” Further, these conventional recommenders typically provide limited ability to interact with the recommender. A user receiving a bad recommendation may know a reason why they do not like the recommendation, but the user is not provided with a method to provide this information to the recommender, and even if they could, a collaborative filtering-based recommender would not know what to do with the information. In addition, these conventional, collaborative filtering-based recommenders are generally not steerable except by applying explicit ratings to items or by applying filters on the metadata returned with the items. In these conventional recommenders, providing a rating may affect future recommendations, but it is difficult to determine, and to explain, how one rating action will affect future recommendations.